import numpy as np #linear algebra
import pandas as pd # data processing,CSV file I/O(e.g pd.read_csv)
import seaborn as sns # for statistical data visualization
import matplotlib.pyplot as mtp # for data visualization
import matplotlib.pyplot as plt
import plotly.express as px
%matplotlib inline

data = pd.read_csv(r"C:\Users\laxma\Downloads\affairs.csv")

data.head()

	Unnamed: 0	rate_marriage	age	yrs_married	children	religious	educ	occupation	occupation_husb	affairs
0	0	3.0	32.0	9.0	3.0	3.0	17.0	2.0	5.0	0.111111
1	1	3.0	27.0	13.0	3.0	1.0	14.0	3.0	4.0	3.230769
2	2	4.0	22.0	2.5	0.0	1.0	16.0	3.0	5.0	1.400000
3	3	4.0	37.0	16.5	4.0	3.0	16.0	5.0	5.0	0.727273
4	4	5.0	27.0	9.0	1.0	1.0	14.0	3.0	4.0	4.666666

data.tail()

	Unnamed: 0	rate_marriage	age	yrs_married	children	religious	educ	occupation	occupation_husb	affairs
6361	6361	5.0	32.0	13.0	2.0	3.0	17.0	4.0	3.0	0.0
6362	6362	4.0	32.0	13.0	1.0	1.0	16.0	5.0	5.0	0.0
6363	6363	5.0	22.0	2.5	0.0	2.0	14.0	3.0	1.0	0.0
6364	6364	5.0	32.0	6.0	1.0	3.0	14.0	3.0	4.0	0.0
6365	6365	4.0	22.0	2.5	0.0	2.0	16.0	2.0	4.0	0.0

data.duplicated().sum()

0

data.columns

Index(['Unnamed: 0', 'rate_marriage', 'age', 'yrs_married', 'children',
       'religious', 'educ', 'occupation', 'occupation_husb', 'affairs'],
      dtype='object')

#VISUALIZATION

plt.bar(data['rate_marriage'],data['age'])
plt.xticks(rotation=90)
plt.show()

fig=px.bar(data,x='yrs_married',y='age',color='yrs_married')
fig.show()

plt.scatter(data['children'],data['rate_marriage'],color='red')
plt.xticks(rotation=90)
plt.show()

plt.figure(figsize=(10,4))
sns.countplot(x='educ', data=data, color='b')
plt.show()

plt.figure(figsize=(10,6))
top_car = data['children'].value_counts().nlargest(10)
sns.countplot(y=data.children,color='cyan')

<AxesSubplot:xlabel='count', ylabel='children'>

sns.lineplot(x='religious', y='Unnamed: 0', data=data)

<AxesSubplot:xlabel='religious', ylabel='Unnamed: 0'>

sns.barplot(data['occupation'],data['age'],color='r')
plt.xticks(rotation=90)
plt.show()

D:\anaconda files\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

plt.figure(figsize=(8, 4))
sns.scatterplot(data=data, x='yrs_married', y='educ')
plt.xlabel('yrs_married')
plt.ylabel('educ')
plt.show()

sns.displot(data["occupation_husb"])

<seaborn.axisgrid.FacetGrid at 0x280c52710a0>

sns.relplot(x='rate_marriage',y='occupation',data=data)

<seaborn.axisgrid.FacetGrid at 0x280c5030640>

sns.countplot(x='occupation',data=data)
plt.xticks(rotation=90)

(array([0, 1, 2, 3, 4, 5]),
 [Text(0, 0, '1.0'),
  Text(1, 0, '2.0'),
  Text(2, 0, '3.0'),
  Text(3, 0, '4.0'),
  Text(4, 0, '5.0'),
  Text(5, 0, '6.0')])

sns.boxplot(x='rate_marriage',y='affairs',data=data)

<AxesSubplot:xlabel='rate_marriage', ylabel='affairs'>

sns.violinplot(x='occupation',y='occupation_husb',data=data)

<AxesSubplot:xlabel='occupation', ylabel='occupation_husb'>

#MODEL BUILDING

x = data.iloc[:,[4,5]].values

import scipy.cluster.hierarchy as shc
dendro = shc.dendrogram(shc.linkage(x, method='ward'))
mtp.title('Dendrogram Plot')
mtp.ylabel('Euclidean Distance')
mtp.xlabel('Customer')
mtp.show()

from sklearn.cluster import AgglomerativeClustering
hc = AgglomerativeClustering(n_clusters=5, affinity='euclidean',linkage='ward')
y_pred=hc.fit_predict(x)

y_pred

array([0, 2, 3, ..., 3, 1, 3], dtype=int64)

mtp.scatter(x[y_pred == 0, 0], x[y_pred == 0,1], s = 100, c = 'blue', label = 'Cluster 1')
mtp.scatter(x[y_pred == 1, 0], x[y_pred == 1,1], s = 100, c = 'red', label = 'Cluster 2')
mtp.scatter(x[y_pred== 2, 0], x[y_pred == 2,1], s = 100, c = 'cyan', label = 'Cluster 3')
mtp.scatter(x[y_pred == 3, 0], x[y_pred == 3,1], s = 100, c = 'black', label = 'Cluster 4')
mtp.scatter(x[y_pred == 4, 0], x[y_pred == 4,1], s = 100, c = 'orange', label = 'Cluster 5')
mtp.title('cluster of yrs married')
mtp.xlabel('Children')
mtp.legend()
mtp.show()